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Analysis of Imbalanced Datasets in the Performance of Deep Learning Approaches for COVID-19 Screening from Chest X-ray Imaging: Impact of Sex and Age Factors
dc.contributor.author | Álvarez-Rodríguez, Lorena | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Ramos, Lucía | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-05-13T11:36:51Z | |
dc.date.available | 2024-05-13T11:36:51Z | |
dc.date.issued | 2023-02-16 | |
dc.identifier.citation | L. Alvarez, J. D. Moura, L. Ramos, J. Novo, y M. Ortega, «Analysis of Imbalanced Datasets in the Performance of Deep Learning Approaches for COVID-19 Screening from Chest X-ray Imaging: Impact of Sex and Age Factors», Kalpa Publications in Computing, vol. 14, pp. 174-177. doi: 10.29007/v25g. | es_ES |
dc.identifier.issn | 2515-1762 | |
dc.identifier.uri | http://hdl.handle.net/2183/36467 | |
dc.description | Comunicación presentada al V Congreso XoveTIC, organizado por el Centro de Investigación en TIC da Universidade da Coruña (CITIC), tendrá lugar los días 5 y 6 de octubre de 2022 | es_ES |
dc.description.abstract | [Absctract]: In this work, we analysed 11 imbalance scenarios with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: (I) Normal vs COVID-19, (II) Pneumonia vs COVID-19 and (III) Non-COVID-19 vs COVID-19. The present study was validated using two representative public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process. The results for the sex-related analysis indicate this factor slightly affects the COVID- 19 deep learning-based systems, although the identified differences are not relevant enough to considerably worsen the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. | es_ES |
dc.description.sponsorship | This research was funded by: Instituto de Salud Carlos III - DTS18/00136; Ministerio de Ciencia e Innovación y Universidades, Gov. of Spain - RTI2018-095894-B-I00; Ministerio de Ciencia e Innovación, Gov. of Spain - PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva - ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia - N845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; N845D 2020/38 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | EasyChair | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLE | es_ES |
dc.relation.uri | https://doi.org/10.29007/v25g | es_ES |
dc.subject | CAD system | es_ES |
dc.subject | Chest X-ray | es_ES |
dc.subject | COVID-19 | es_ES |
dc.subject | Deep learning | es_ES |
dc.title | Analysis of Imbalanced Datasets in the Performance of Deep Learning Approaches for COVID-19 Screening from Chest X-ray Imaging: Impact of Sex and Age Factors | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Kalpa Publications in Computing | es_ES |
UDC.volume | 14 | es_ES |
UDC.startPage | 174 | es_ES |
UDC.endPage | 177 | es_ES |
UDC.conferenceTitle | XoveTIC V | es_ES |